Because producing correct entailment trees from a corpus is challenging,
we define three tasks of increasing difficulty (named no-distractor, distractor, and full-corpus, respectively) that simplify the retrieval and evaluation problems inherent in the task.
The inputs to all three are a hypothesis \(H\) , namely a declarative form of a question + answer (\(QA\) ),For convenience we provide both \(H\)  and \(QA\)  as inputs, although in principle \(H\)  may be generated
from \(QA\)  automatically, e.g., using the QA2D model [1]}
and some sentences \(S\)  expressing (both relevant and irrelevant) knowledge about the world.
The desired output is a valid entailment tree \(T\)  where the leaves are sentences selected from \(S\) ,
the intermediate nodes int\(_i\)  are intermediate conclusions (new sentences, not part of the input), and the
root node (conclusion) is the hypothesis \(H\) . \(T\)  is valid if every node \(n_i\)  in the tree
is entailed by its children. Examples are shown in Figures REF  and REF .
